setwd("D:/BINUS/Semester4/DataMiningVisualization/FinalProject")
df <- read.csv('Crime_Data_from_2020_to_Present.csv')
head(df)
## DR_NO Date.Rptd DATE.OCC TIME.OCC AREA
## 1 190326475 03/01/2020 12:00:00 AM 03/01/2020 12:00:00 AM 2130 7
## 2 200106753 02/09/2020 12:00:00 AM 02/08/2020 12:00:00 AM 1800 1
## 3 200320258 11/11/2020 12:00:00 AM 11/04/2020 12:00:00 AM 1700 3
## 4 200907217 05/10/2023 12:00:00 AM 03/10/2020 12:00:00 AM 2037 9
## 5 220614831 08/18/2022 12:00:00 AM 08/17/2020 12:00:00 AM 1200 6
## 6 231808869 04/04/2023 12:00:00 AM 12/01/2020 12:00:00 AM 2300 18
## AREA.NAME Rpt.Dist.No Part.1.2 Crm.Cd
## 1 Wilshire 784 1 510
## 2 Central 182 1 330
## 3 Southwest 356 1 480
## 4 Van Nuys 964 1 343
## 5 Hollywood 666 2 354
## 6 Southeast 1826 2 354
## Crm.Cd.Desc Mocodes Vict.Age
## 1 VEHICLE - STOLEN 0
## 2 BURGLARY FROM VEHICLE 1822 1402 0344 47
## 3 BIKE - STOLEN 0344 1251 19
## 4 SHOPLIFTING-GRAND THEFT ($950.01 & OVER) 0325 1501 19
## 5 THEFT OF IDENTITY 1822 1501 0930 2004 28
## 6 THEFT OF IDENTITY 1822 0100 0930 0929 41
## Vict.Sex Vict.Descent Premis.Cd Premis.Desc
## 1 M O 101 STREET
## 2 M O 128 BUS STOP/LAYOVER (ALSO QUERY 124)
## 3 X X 502 MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)
## 4 M O 405 CLOTHING STORE
## 5 M H 102 SIDEWALK
## 6 M H 501 SINGLE FAMILY DWELLING
## Weapon.Used.Cd Weapon.Desc Status Status.Desc Crm.Cd.1 Crm.Cd.2 Crm.Cd.3
## 1 NA AA Adult Arrest 510 998 NA
## 2 NA IC Invest Cont 330 998 NA
## 3 NA IC Invest Cont 480 NA NA
## 4 NA IC Invest Cont 343 NA NA
## 5 NA IC Invest Cont 354 NA NA
## 6 NA IC Invest Cont 354 NA NA
## Crm.Cd.4 LOCATION Cross.Street LAT
## 1 NA 1900 S LONGWOOD AV 34.0375
## 2 NA 1000 S FLOWER ST 34.0444
## 3 NA 1400 W 37TH ST 34.0210
## 4 NA 14000 RIVERSIDE DR 34.1576
## 5 NA 1900 TRANSIENT 34.0944
## 6 NA 9900 COMPTON AV 33.9467
## LON
## 1 -118.3506
## 2 -118.2628
## 3 -118.3002
## 4 -118.4387
## 5 -118.3277
## 6 -118.2463
df <- df[, !(names(df) %in% c('Crm.Cd.2', 'Crm.Cd.3', 'Crm.Cd.4', 'Cross.Street'))]
df <- df[!(is.na(df$Weapon.Used.Cd) | is.na(df$Weapon.Desc)),]
dim(df)
## [1] 324477 24
library(plotly)
## Warning: package 'plotly' was built under R version 4.3.3
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.3.3
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.3.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
library(sf)
## Warning: package 'sf' was built under R version 4.3.3
## Linking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUE
get_mode <- function(v) {
uniq_vals <- unique(v)
uniq_vals[which.max(tabulate(match(v, uniq_vals)))]
}
df$TIME.OCC <- as.numeric(df$TIME.OCC)
df[['Time.h']] = ((df$TIME.OCC - df$TIME.OCC %% 100) / 100) %% 24
loc = df[df$LAT != 0,c('LAT', 'LON')]
gjson <-"./los-angeles-ca_.geojson"
border <- st_read(gjson)
## Reading layer `los-angeles-ca_' from data source
## `D:\BINUS\Semester4\DataMiningVisualization\FinalProject\los-angeles-ca_.geojson'
## using driver `GeoJSON'
## Simple feature collection with 114 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -118.6682 ymin: 33.70454 xmax: -118.1553 ymax: 34.33731
## Geodetic CRS: WGS 84
ggplot() +
geom_sf(data = border) +
geom_point(data=loc, aes(x=LON, y=LAT), size=0.1, alpha=0.025, color='red') +
geom_sf(data = border, fill=NA) +
theme_minimal() +
labs(title = "Map of Crime Locations in Los Angeles", x="Latitude", y="Longitude")
df <- df %>%
mutate(Vict.Age = na_if(Vict.Age, 0)) %>%
mutate(Vict.Age = na_if(Vict.Age, -1)) %>%
mutate(Vict.Age = na_if(Vict.Age, -2)) %>%
mutate(Vict.Age = na_if(Vict.Age, -4))
victim.age <- df %>%
group_by(Vict.Age) %>%
summarize(count = n())
fig <- plot_ly(x = ~df$Vict.Age, type = "histogram")
fig <- fig %>% layout(title = 'Victim Age', xaxis=list(title='Victim Age'))
fig
## Warning: Ignoring 26011 observations
df[["weapon.generalized"]] <- NA
df$weapon.generalized[df$Weapon.Used.Cd >= 100 & df$Weapon.Used.Cd < 200] <- "Firearm"
df$weapon.generalized[df$Weapon.Used.Cd >= 200 & df$Weapon.Used.Cd < 300] <- "Sharp Object"
df$weapon.generalized[df$Weapon.Used.Cd >= 300 & df$Weapon.Used.Cd < 400] <- "Blunt Object"
df$weapon.generalized[df$Weapon.Used.Cd >= 400 & df$Weapon.Used.Cd < 500] <- "Physical"
df$weapon.generalized[df$Weapon.Used.Cd >= 500 & df$Weapon.Used.Cd < 600] <- "Other/Unknown Weapon"
df$weapon.generalized[df$Weapon.Used.Cd == 511] <- "Treat"
df$weapon.generalized[df$Weapon.Used.Cd == 501] <- "Treat"
df$weapon.generalized[df$Weapon.Used.Cd == 515] <- "Physical"
crm = read.csv('categories.csv', sep=';')
crm <- crm[c('Crm.Cd', 'Crm.Category')]
df <- df%>%
left_join(crm, by="Crm.Cd")
crime <- df %>%
group_by(Crm.Category) %>%
summarize(count = n(), .groups='drop')
colors <- c("#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf", "#ff0000", "#00ff00", "#000", "#f3f3fd")
fig <- plot_ly(crime, labels=~Crm.Category, values=~count, type='pie', marker = list(colors = colors)) %>%
layout(title = "Distribution of Crime Categories")
fig
df$Vict.Sex <- ifelse(df$Vict.Sex == '', get_mode(df$Vict.Sex), df$Vict.Sex)
df <- df %>%
mutate(Vict.Sex = recode(Vict.Sex,
"M" = "Male",
"F" = "Female",
"H" = "Others",
"X" = "Others"
))
victim.sex <- df %>%
group_by(Vict.Sex) %>%
summarize(count = n())
fig <- plot_ly(victim.sex, labels=~Vict.Sex, values=~count, type='pie')
fig <- fig %>% layout(title = 'Victim Sex',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
fig
weapon <- df %>%
group_by(weapon.generalized)%>%
summarize(total = n(), .groups="drop")
weapon_used <- df %>%
group_by(weapon.generalized, Vict.Sex) %>%
summarize(count = n(), .groups="drop") %>%
left_join(weapon, by="weapon.generalized") %>%
mutate(perc = count/total*100)
fig <- plot_ly(weapon_used, x=~weapon.generalized, y=~perc, color=~Vict.Sex, type='bar') %>%
layout(
title = "Relation Between Used Weapon and Victim Gender",
xaxis = list(title='Weapon Used'),
yaxis = list(title = 'Percentage'),
barmode="group"
)
fig
count.by.time <- df %>%
group_by(Time.h) %>%
summarize(total = n(), .groups="drop")
time.occurred <- df %>%
group_by(weapon.generalized, Time.h) %>%
summarize(count = n(), .groups='drop') %>%
filter(weapon.generalized != 'Other/Unknown Weapon') %>%
left_join(count.by.time, by="Time.h") %>%
mutate(perc = count/total*100)
fig<-plot_ly(time.occurred, x = ~Time.h, y=~perc , color=~weapon.generalized, type="scatter", mode="lines") %>%
layout(title="Percentage of Used Weapon over time", xaxis = list(title="Time in Hours"), yaxis = list(title="Percentage"))
fig
color.def <- c("#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22")
vs <- df %>%
group_by(Vict.Sex) %>%
summarize(total=n(), .groups="drop")
dist <- df %>%
group_by(Time.h, Vict.Sex) %>%
summarize(count=n(), .groups="drop") %>%
left_join(vs, by="Vict.Sex") %>%
mutate(perc = count/total*100)
fig <- plot_ly(dist, x=~Time.h,y=~perc, color=~Vict.Sex, colors = color.def, type="scatter", mode="line") %>%
layout(barmode = "overlay",
title = "Distribution of time by Victim Sex",
xaxis = list(title = "Time in hour"),
yaxis = list(title="Percentage")
)
fig
desc <- df %>%
group_by(weapon.generalized, Crm.Category) %>%
summarize(count = log(n()), .groups = 'drop') %>%
filter(weapon.generalized != 'Other/Unknown Weapon')
plt <- plot_ly(desc, x=~weapon.generalized, y=~Crm.Category, z=~count ,type="heatmap") %>%
layout(
title="Heatmap of Weapon and Criminal Category (Natural Log Transformed)",
xaxis=list(title="Weapon Category"),
yaxis=list(title="Criminal Category"),
showlegend=FALSE
)
plt